Intelligence In Radiology Research Articles

A white paper on artificial intelligence in radiology, getting over the hype

A white paper on artificial intelligence in radiology, getting over the hype

A preliminary glossary of artificial intelligence in radiology.

A preliminary glossary of artificial intelligence in radiology.

Diffusion-weighted imaging-diagnostic supplement or alternative to contrast agents in early detection of malignancies?

Medical research in the field of oncologic imaging diagnostics using magnetic resonance imaging increasingly includes diffusion-weighted imaging (DWI) sequences. The DWI sequences allow insights into different microstructural diffusion properties of water molecules in tissues depending on the sequence modification used and enable visual and quantitative analysis of the acquired imaging data. In DWI, the application of intravenous gadolinium-containing contrast agents is unnecessary and only the mobility of naturally occurring water molecules in tissues is quantified. These characteristics predispose DWI as apotential candidate for emerging as an independent diagnostic tool in selected cases and specific points in question. Current clinical diagnostic studies and the ongoing technical developments, including the increasing influence of artificial intelligence in radiology, support the growing importance of DWI. Especially with respect to selective approaches for early detection of malignancies, DWI could make an essential contribution as an eligible diagnostic tool; however, prior to discussing abroader clinical implementation, challenges regarding reliable data quality, standardization and quality assurance must be overcome.

Impact of the rise of artificial intelligence in radiology: What do radiologists think?

The purpose of this study was to assess the perception, knowledge, wishes and expectations of a sample of French radiologists towards the rise of artificial intelligence (AI) in radiology. A general data protection regulation-compliant electronic survey was sent by e-mail to the 617 radiologists registered in the French departments of Nord and Pas-de-Calais (93 radiology residents and 524 senior radiologists), from both public and private institutions. The survey included 42 questions focusing on AI in radiology, and data were collected between January 16th and January 31st, 2019. The answers were analyzed together by a senior radiologist and a radiology resident. A total of 70 radiology residents and 200 senior radiologists participated to the survey, which corresponded to a response rate of 43.8% (270/617). One hundred ninety-eight radiologists (198/270; 73.3%) estimated they had received insufficient previous information on AI. Two hundred and fifty-five respondents (255/270; 94.4%) would consider attending a generic continuous medical education in this field and 187 (187/270; 69.3%) a technically advanced training on AI. Two hundred and fourteen respondents (214/270; 79.3%) thought that AI will have a positive impact on their future practice. The highest expectations were the lowering of imaging-related medical errors (219/270; 81%), followed by the lowering of the interpretation time of each examination (201/270; 74.4%) and the increase in the time spent with patients (141/270; 52.2%). While respondents had the feeling of receiving insufficient previous information on AI, they are willing to improve their knowledge and technical skills on this field. They share an optimistic view and think that AI will have a positive impact on their future practice. A lower risk of imaging-related medical errors and an increase in the time spent with patients are among their main expectations.

Fostering a Healthy AI Ecosystem for Radiology: Conclusions of the 2018 RSNA Summit on AI in Radiology.

The 2018 RSNA Summit on AI in Radiology brought together a diverse group of stakeholders to identify and prioritize areas of need related to artificial intelligence in radiology. This article presents the proceedings of the summit with emphasis on RSNA's role in leading, organizing, and catalyzing change during this important time in radiology. © RSNA, 2019.

Artificial intelligence in radiology: friend or foe? Where are we now and where are we heading?

The use of artificial intelligence (AI) has been rapidly progressing in medicine, particularly in radiology. AI has also been the source of great innovation and a prominent topic of discussion within radiology societies and ground-breaking research in recent years.Despite the risks and quality assurance issues involved in AI, it offers tremendous opportunities for changing how radiological services are delivered. Moreover, it is conceivable that AI could become a reliable, hard-working friend to the radiologist rather than a foe, in addition to being a useful training tool for radiologist trainees.

Artificial intelligence in radiology

Artificial intelligence in radiology

The FDA and Artificial Intelligence in Radiology: Defining New Boundaries

The FDA and Artificial Intelligence in Radiology: Defining New Boundaries

Deep learning and artificial intelligence in radiology: Current applications and future directions.

Deep learning and artificial intelligence in radiology: Current applications and future directions.

Artificial Intelligence in Radiology: Current Technology and Future Directions.

Artificial intelligence (AI) has been heralded as the next big wave in the computing revolution and touted as a transformative technology for many industries including health care. In radiology, considerable excitement and anxiety are associated with the promise of AI and its potential to disrupt the practice of the radiologist. Radiology has often served as the gateway for medical technological advancements, and AI will likely be no different. We present a brief overview of AI advancements that have driven recent interest, offer a review of the current literature, and examine the most likely ways that AI will change radiology in the coming years.

Ethics, Artificial Intelligence, and Radiology

The rapid evolution of autonomous and intelligent systems in radiology that make predictions and drive actions independent of direct human oversight is reason for the ACR to investigate the ethics and code of behavior for computers and artificial intelligence in radiology.

Artificial intelligence in radiology.

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

Artificial intelligence in radiology: Hype or hope?

Artificial intelligence in radiology: Hype or hope?

Machine Learning and Artificial Intelligence in Radiology

If you are a young radiologist, let me walk you through what your life could look like in about 10 to 15 years:

From Images to Actions: Opportunities for Artificial Intelligence in Radiology.

Radiologists play an important role in guiding, vetting, and incorporating artificial intelligence systems into clinical practice.

When Is the Right Time to Conduct a Clinical Trial of a Diagnostic Imaging Technology?

HomeRadiologyVol. 248, No. 1 PreviousNext Reviews and CommentaryEditorialsWhen Is the Right Time to Conduct a Clinical Trial of a Diagnostic Imaging Technology?Bruce J. Hillman, Constantine A. GatsonisBruce J. Hillman, Constantine A. GatsonisAuthor Affiliations1From the Department of Radiology, the University of Virginia Health System, PO Box 800170, Charlottesville, VA 22908 (B.J.H.); and Center for Statistical Sciences, Program in Public Health, Brown University, Providence, RI (C.A.G.). Received December 21, 2007; final version accepted February 21, 2008.Address correspondence to B.J.H. (e-mail: [email protected]).Bruce J. HillmanConstantine A. GatsonisPublished Online:Jul 1 2008https://doi.org/10.1148/radiol.2481072190MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In References1 U.S. Food and Drug Administration, Center for Drug Evaluation and Research. Guidance documents. http://www.fda.gov/cder/guidance/index.htm. Accessed December 18, 2007. Google Scholar2 Fryback DG, Thornbury JR. Efficacy of diagnostic imaging. Med Decis Making 1991;11:88–94. Crossref, Medline, Google Scholar3 Gatsonis C. Design of evaluations of imaging technologies: development of a paradigm. Acad Radiol 2000;7:681–683. Crossref, Medline, Google Scholar4 Hillman BJ, Gatsonis C, Schnall MD. The American College of Radiology Imaging Network (ACRIN): a retrospective on 5 years of conducting multi-center trials of radiology and plans for the future. J Am Coll Radiol 2004;1:346–350. Crossref, Medline, Google Scholar5 Ziegler KM, Flamm CR, Aronson N. The Blue Cross Blue Shield Technology Evaluation Center: how we evaluate radiology technologies. J Am Coll Radiol 2005;2:33–38. Crossref, Medline, Google Scholar6 Chalmers TC. The clinical trial. Milbank Mem Fund Q Health Soc 1981;59:324–329. Crossref, Medline, Google Scholar7 Phelps CE, Mushlin AI. Focusing technology assessment using medical decision theory. Med Decis Making 1988;8:279–289. Crossref, Medline, Google Scholar8 Kahan JP, Neu CR, Hammons GT, Hillman BJ. The decision to initiate clinical trials of current medical practices. Publication no. R-3289-NCHSR. Santa Monica, Calif: Rand Corporation, 1985. Google Scholar9 Pisano ED, Gatsonis CA, Yaffe MJ, et al. American College of Radiology Imaging Network Digital Mammographic Imaging Screening Trial: objectives and methodology. Radiology 2005;236:404–412. Link, Google Scholar10 Pisano ED, Gatsonis C, Hendrick E, et al. Diagnostic performance of digital versus film mammography for breast-cancer screening. N Engl J Med 2005;353:1773–1783. Crossref, Medline, Google Scholar11 Hillman BJ, Kahan JP. Initiating clinical trials: a case study of breast cancer treatment. Publication no. N-2320/3-NCHSR. Santa Monica, Calif: Rand Corporation, 1985. Google ScholarArticle HistoryPublished in print: 2008 FiguresReferencesRelatedDetailsCited ByEuropean Journal of Nuclear Medicine and Molecular Imaging, Vol. 46, No. 1European Journal of Radiology, Vol. 103Academic Radiology, Vol. 24, No. 8Journal of Biomedical Science, Vol. 23, No. 1Annals of Operations Research, Vol. 230, No. 1Journal of Nuclear Medicine, Vol. 53, No. 5Journal of the American College of Radiology, Vol. 8, No. 2Consensus Interpretation in Imaging Research: Is There a Better Way?1Alexander A. Bankier, , Deborah Levine, , Elkan F. Halpern, , and Herbert Y. Kressel, 1 October 2010 | Radiology, Vol. 257, No. 1European Radiology, Vol. 20, No. 1The AAPS Journal, Vol. 11, No. 2Recommended Articles Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?Radiology2022Volume: 0Issue: 0Guidance for Interventional Trials Involving Artificial IntelligenceRadiology: Artificial Intelligence2020Volume: 2Issue: 6Continuous Learning AI in Radiology: Implementation Principles and Early ApplicationsRadiology2020Volume: 297Issue: 1pp. 6-14Machine Learning for Workflow Applications in Screening Mammography: Systematic Review and Meta-AnalysisRadiology2021Volume: 302Issue: 1pp. 88-104Radiology Resident Assessment and Feedback DashboardRadioGraphics2018Volume: 38Issue: 5pp. 1443-1453See More RSNA Education Exhibits Utilization of Imaging in Clinical Trials Under Current FDA GuidelinesDigital Posters2020Coming Out: Making Your Breast Imaging Center LGBT FriendlyDigital Posters2018Embracing EQUIP in Our Daily Mammography Environment: Best Practices and Lessons Learned (The EQUIP Way or the Highway)Digital Posters2018 RSNA Case Collection Acute breast hematomaRSNA Case Collection2020NeurocysticercosisRSNA Case Collection2022Invasive ductal carcinoma as developing asymmetryRSNA Case Collection2021 Vol. 248, No. 1 Supplemental MaterialMetrics Altmetric Score PDF download

Artificial intelligence in radiology: decision support systems.

Computer-based systems that incorporate artificial intelligence techniques can help physicians make decisions about their patients' care. In radiology, systems have been developed to help physicians choose appropriate radiologic procedures and to formulate accurate diagnoses. These decision support systems use techniques such as rule-based reasoning, artificial neural networks, hypertext, Bayesian networks, and case-based reasoning. This article reviews these artificial intelligence techniques, describes their application in radiology, and discusses the role that decision support systems may play in radiology's future.